This is a longitudinal, observational study, which does not include any intervention. There are three study groups. The first group consists of BLSA participants without known coronary disease; the second consists of individuals with a coronary artery calcium score less than the 25th percentile for their age and gender; the third group consists of individuals with premature coronary artery disease (diagnosis made at age <60 in females and <50 in males) or with a coronary calcium score which is greater than the 50th percentile for their age and gender. Participants in the second and third group listed above are recruited from the Johns Hopkins Medical Institutions. Recruitment has been completed and the longitudinal portion of the study is ongoing with 80 percent of all second visits completed. Analysis of the first visits is being conducted to assess cross sectional findings which can then be expanded to a longitudinal analysis once all second visits are completed. Preliminary longitudinal analysis of data from a subsample that had completed repeated measures was underpowered to detect longitudinal changes. Currently, repeated visits are being performed on the remaining eligible participants per the original power analysis calculations. The future analytic plan: 1. Perform cross-sectional regression analysis to predict age using vascular parameters and construct the prediction formula from the BLSA cohort with Calcium score within 25-75% percentile. The predicted value is referred to as arterial age. 2. Apply the equation to predict vascular age for the successful and accelerated aging group and then examine the relationship between chronological and arterial age 3. Repeat the analysis on the longitudinal data using follow-up time (aging) instead of age and the change of the arterial parameters instead of the cross-sectional value. The predicted values would represent arterial aging and would be correlated with vascular aging in the different groups. 4. Identify BLSA participants with good arterial aging based on single variables and on groups of variables. Find subjects in other groups who have similar profiles on the selected variables.